Transcriptomic Profiling of Klotho mouse models
Ravi Pandey
04 January, 2024
knitr::opts_knit$set(root.dir = ".")
knitr::opts_chunk$set(message=FALSE, warning=FALSE, error=FALSE, echo = FALSE)Model Overview
This document reports on the processing of RNAseq data for Klotho FC (susceptible) and Klotho VS (resistant) mouse models on C57BL/6J background.
Klotho-F/C mice carry the S370C point mutation in exon 2 of the Klotho gene, homologous to the human late-onset Alzheimer’s disease “risk” configuration of p.F352 and p.C370. To create the KL-F/C allele, CRISPR/Cas9 endonuclease-mediated genome editing of Kl exon 2 was used to introduce a S370C missense mutation that corresponds to the human C370 codon associated with LOAD. This is codon S372 in the mouse.
Klotho-V/S mice carry the human F352V variant is associated with decreased late-onset Alzheimer’s disease (LOAD)-associated amyloid plaque burden in patients who also carry the APOE4 polymorphism, but not APOE3. To create the KL-V/S allele, CRISPR/Cas9 endonuclease-mediated genome editing of Kl exon 2 was used to introduce a F352V missense mutation that corresponds to a human mutation associated with decreased susceptibility to LOAD.This is codon F354 in the mouse.
Validated Study Population
| Sex | Genotype | Age | n |
|---|---|---|---|
| Female | B6 | 4 | 8 |
| Female | B6 | 12 | 9 |
| Female | Klotho(FC).HET | 4 | 4 |
| Female | Klotho(FC).HET | 12 | 4 |
| Female | Klotho(FC).HOM | 4 | 4 |
| Female | Klotho(FC).HOM | 12 | 5 |
| Female | Klotho(VS).HET | 4 | 3 |
| Female | Klotho(VS).HET | 12 | 6 |
| Female | Klotho(VS).HOM | 4 | 7 |
| Female | Klotho(VS).HOM | 12 | 5 |
| Male | B6 | 4 | 10 |
| Male | B6 | 12 | 15 |
| Male | Klotho(FC).HET | 4 | 5 |
| Male | Klotho(FC).HET | 12 | 5 |
| Male | Klotho(FC).HOM | 4 | 6 |
| Male | Klotho(FC).HOM | 12 | 7 |
| Male | Klotho(VS).HET | 4 | 4 |
| Male | Klotho(VS).HET | 12 | 3 |
| Male | Klotho(VS).HOM | 4 | 4 |
| Male | Klotho(VS).HOM | 12 | 7 |
total number of samples:
## n
## 1 121## NULL- Raw fastq files of
B6.Klotho(VS)&B6.Klotho(FC)were generated by Jax GT and delivered as part of submissions 23-model-ad-002; 23-model-ad-002-run2, and 23-model-ad-002-run3.
All fastq files are processed using the nextflow-core rnaseq pipeline on sumner. Reads were aligned to a custom LOAD2 reference genome as a directional library with reverse strandedness. Raw count data,validated metadata, and data pre-processing workflow are uploaded to MODEL-AD Workspace on Synapse.
For more information about MODEL AD
Expression Plots
Principal Component Analysis
Differential Analysis
Now, after exploring and formatting the data, We will look for differential expression using DESeq2 in mouse models homoyzgous for Klotho variants.
Final Study Population
| Sex | Genotype | Age | n |
|---|---|---|---|
| Female | B6 | 4 | 8 |
| Female | B6 | 12 | 9 |
| Female | Klotho(FC).HOM | 4 | 4 |
| Female | Klotho(FC).HOM | 12 | 5 |
| Female | Klotho(VS).HOM | 4 | 7 |
| Female | Klotho(VS).HOM | 12 | 5 |
| Male | B6 | 4 | 10 |
| Male | B6 | 12 | 15 |
| Male | Klotho(FC).HOM | 4 | 6 |
| Male | Klotho(FC).HOM | 12 | 7 |
| Male | Klotho(VS).HOM | 4 | 4 |
| Male | Klotho(VS).HOM | 12 | 7 |
total number of samples:
## n
## 1 87Differentially Expressed Genes
DEGs in Klotho models compared to B6
total number of differentially expressed genes at adjP<0.05 |
||
| comparison | Up_DEGs | Down_DEGs |
|---|---|---|
| Klotho(FC).HOM-Female-4M vs B6-Female-4M | 0 | 0 |
| Klotho(FC).HOM-Male-4M vs B6-Male-4M | 3 | 1 |
| Klotho(FC).HOM-Female-12M vs B6-Female-12M | 0 | 0 |
| Klotho(FC).HOM-Male-12M vs B6-Male-12M | 0 | 0 |
| Klotho(VS).HOM-Female-4M vs B6-Female-4M | 0 | 1 |
| Klotho(VS).HOM-Male-4M vs B6-Male-4M | 1 | 0 |
| Klotho(VS).HOM-Female-12M vs B6-Female-12M | 0 | 1 |
| Klotho(VS).HOM-Male-12M vs B6-Male-12M | 0 | 0 |
DEGs in Klotho(VS) compared to Klotho(FC)
total number of differentially expressed genes at adjP<0.05 |
||
| comparison | Up_DEGs.pval.05 | Down_DEGs.pval.05 |
|---|---|---|
| Klotho(VS).HOM-Female-4M vs Klotho(FC).HOM-Female-4M | 2 | 1 |
| Klotho(VS).HOM-Male-4M vs Klotho(FC).HOM-Male-4M | 0 | 3 |
| Klotho(VS).HOM-Female-12M vs Klotho(FC).HOM-Female-12M | 16 | 27 |
| Klotho(VS).HOM-Male-12M vs Klotho(FC).HOM-Male-12M | 455 | 422 |
In the next sections we performed functional analysis for these differentially expressed genes as well as correlate them with human AD data.
Functional Analysis
Over-representation (or enrichment) analysis is a statistical method that determines whether genes from pre-defined sets (ex: those beloging to a specific GO term or KEGG pathway) are present more than would be expected (over-represented) in a subset of your data. In this case, the subset is your set of under or over expressed genes.
KEGG Pathways Enrichment
We look for enrichment of biological pathways in a list of differentially expressed genes. Here we test for enrichment of KEGG pathways using using enrichKEGG function in [clusterProfiler] (https://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html) package.
using differentially expressed genes in Klotho models from comparison with age and sex-matched B6**
GeneSet Enrichment Analysis (GSEA) for KEGG Pathways
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a pre-defined set of genes (ex: those beloging to a specific GO term or KEGG pathway) shows statistically significant, concordant differences between two biological states.
Using differential analysis results compare to B6 mice
Biodomain Plots
Human-mouse correlation
Mouse variant’s vs Biological domain
12 months old Klotho homozygous models
## [1] -0.1956513 0.2507656
Mouse variant’s vs Subdomains
12 months old Klotho homozygous models
## [1] -0.6620526 0.5079372
Biological Domain Annotations
Annotation of Enriched GO-terms with Biological Domains
Klotho homozygous models vs B6
We characterized the GO enrichments with the biological domain annotations and see if we can get more context about what is changing in that model. We’ll focus on the ORA results and start by annotating the results with biodomain groupings.
Not all of the enriched terms are annotated to a biological domain. Some terms are too broad and not specific (e.g. ‘defense response’), while others may not have been captured by a biological domain annotation yet (e.g. ‘regulation of immune system process’). Remember that the conception of the biodomains involved a requirement that they be modifiable, and these terms may be added to the biodomain in the future.
Biological Sub-Domain Annotation
Annotation of Enriched GO-terms with Biological Sub-Domains
Klotho homozygous models vs B6
AMP-AD Module Correlation
Wan, et al. performed multi method co-expression network analysis followed by differential analysis and found 30 co-expression modules related LOAD pathology from human cohort study. Among the 30 aggregate co-expression modules, five consensus clusters have been described by Wan, et al. These consensus clusters consist of a subset of modules which are associated with similar AD related changes across the multiple studies and brain regions.
There are two approaches that we adopted to compute correlation between mouse data with human AD modules:
Correlation with human AD modules using logFC values
- Compare change in expression in Human AD cases with change in
expression in mouse models for each orthologous gene in a given module
- LogFC(h) = log fold change in transcript expression of human AD patients compared to control patients.
- LogFC(m) = log fold change in transcript expression of mouse AD models compare to control mouse models. \[cor.test(LogFC(h), LogFC(m))\]
Klotho homozygous models compared to age and sex-matched B6 controls
Correlation between effect of variants and human AD modules
- Compare Human AD to mouse genetic effects for each orthologous gene
in a given module
- h = human gene expression (Log2 RNA-seq Fold Change control/AD)
- β = mouse gene expression effect from linear regression model (Log2 RNA-seq TPM) \[cor.test(LogFC(h), β)\]
All ages combined
4 month-old mouse model
12 month-old mouse model
## [1] -0.2123427 0.1797888
AD subtypes Correlation
We also computed correlation with AD subtypes in ROSMAP, Mayo and MSBB cohort identified by Nikhil et al. and five AD subtypes in MSBB cohort identified by Neff et al.. Nikhil’s subtype’s were annotated as inflammatory(ROSMAP_subtypeA, Mayo_subtypeA & B, MSBB_subtypeA) and non-inflammatory subtypes. Neff’s subtypes were classified into three larger classes: typical AD (subtype C1 & C2), intermediate (subtype B1 & B2), or atypical AD (subtype A).
Correlation with Nikhil’s subtypes using logFC values
All Klotho models compared to age and sex-matched B6
Correlation with Neff’s subtypes using logFC values
All Klotho mouse models compared to age and sex-matched B6
## [1] -0.1484528 0.1510059
Correlation between effect of variants and Nikhil’s subtypes
## [1] -0.1661101 0.1686196
Age-wise Correlation between effect of variants and Nikhil’s subtypes
## [1] -0.1277351 0.1408664## [1] -0.1549459 0.1496708
Correlation between effect of variants and Neff’s subtypes
## [1] -0.1901044 0.1824777
Correlation between age-wise effect of variants and Neff’s subtypes
## [1] -0.1442864 0.1407174## [1] -0.1461507 0.1662225
Pseudostate Correlation
Staging of Alzheimer’s Disease (AD) was inferred using bulk RNA-Seq data generated from post-mortem brain homogenate samples from the ROS/MAP study. Identified seven subtypes of LOAD from Female RNA-seq and six subtypes from Male RNA-Seq (i.e., branches), suggesting the LOAD populations should be stratified by better biomarkers with tailored treatment strategies.
We compared change in expression in our mouse models with change in expression in patients in each pseudostaes compared to pseudostates 1(i.e. controls).
Correlation with Female Pseudostate data
Klotho homozygous mouse models compared to B6
Correlation with Male Pseudostate data
Correlation between male pseudostates and mouse variants effect
Correlation between female pseudostates and mouse variants effect
Session Info
current session info
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.2.2 (2022-10-31)
os macOS Ventura 13.5.2
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-01-04
pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0)
annotate 1.76.0 2022-11-07 [1] Bioconductor
AnnotationDbi * 1.60.2 2023-03-12 [1] Bioconductor
ape 5.7-1 2023-03-13 [1] CRAN (R 4.2.0)
aplot 0.2.0 2023-08-09 [1] CRAN (R 4.2.0)
backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0)
beachmat 2.14.2 2023-04-09 [1] Bioconductor
Biobase * 2.58.0 2022-11-07 [1] Bioconductor
BiocGenerics * 0.44.0 2022-11-07 [1] Bioconductor
BiocParallel * 1.32.6 2023-03-19 [1] Bioconductor
BiocSingular 1.14.0 2022-11-07 [1] Bioconductor
Biostrings 2.66.0 2022-11-07 [1] Bioconductor
bit 4.0.5 2022-11-15 [1] CRAN (R 4.2.0)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.2.0)
blob 1.2.4 2023-03-17 [1] CRAN (R 4.2.0)
broom 1.0.5 2023-06-09 [1] CRAN (R 4.2.0)
bslib 0.5.0 2023-06-09 [1] CRAN (R 4.2.0)
cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
car 3.1-2 2023-03-30 [1] CRAN (R 4.2.0)
carData 3.0-5 2022-01-06 [1] CRAN (R 4.2.0)
checkmate 2.2.0 2023-04-27 [1] CRAN (R 4.2.0)
circlize * 0.4.15 2022-05-10 [1] CRAN (R 4.2.0)
cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
clipr 0.8.0 2022-02-22 [1] CRAN (R 4.2.0)
clue 0.3-64 2023-01-31 [1] CRAN (R 4.2.0)
cluster 2.1.4 2022-08-22 [2] CRAN (R 4.2.2)
clusterProfiler * 4.7.1.001 2023-03-06 [1] Github (YuLab-SMU/clusterProfiler@a0eb9bc)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.2.2)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
commonmark 1.9.0 2023-03-17 [1] CRAN (R 4.2.0)
ComplexHeatmap * 2.15.1 2023-01-03 [1] Github (jokergoo/ComplexHeatmap@7f95b79)
corrplot * 0.92 2021-11-18 [1] CRAN (R 4.2.0)
cowplot * 1.1.1 2020-12-30 [1] CRAN (R 4.2.0)
crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
data.table * 1.14.8 2023-02-17 [1] CRAN (R 4.2.0)
DBI 1.1.3 2022-06-18 [1] CRAN (R 4.2.0)
DelayedArray 0.24.0 2022-11-07 [1] Bioconductor
DelayedMatrixStats 1.20.0 2022-12-04 [1] Bioconductor
desc 1.4.2 2022-09-08 [1] CRAN (R 4.2.0)
DESeq2 * 1.38.3 2023-01-22 [1] Bioconductor
details 0.3.0 2022-03-27 [1] CRAN (R 4.2.0)
digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.0)
doParallel 1.0.17 2022-02-07 [1] CRAN (R 4.2.0)
DOSE * 3.24.2 2022-11-23 [1] Bioconductor
downloader 0.4 2015-07-09 [1] CRAN (R 4.2.0)
dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0)
dqrng 0.3.0 2021-05-01 [1] CRAN (R 4.2.0)
edgeR 3.40.2 2023-01-22 [1] Bioconductor
EnhancedVolcano * 1.16.0 2022-11-07 [1] Bioconductor
enrichplot * 1.18.4 2023-04-02 [1] Bioconductor
evaluate 0.21 2023-05-05 [1] CRAN (R 4.2.0)
fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.0)
farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.0)
fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
fastmatch 1.1-3 2021-07-23 [1] CRAN (R 4.2.0)
fgsea 1.24.0 2022-11-07 [1] Bioconductor
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.0)
foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.0)
foreign 0.8-83 2022-09-28 [2] CRAN (R 4.2.2)
formatR 1.14 2023-01-17 [1] CRAN (R 4.2.0)
Formula 1.2-5 2023-02-24 [1] CRAN (R 4.2.0)
futile.logger * 1.4.3 2016-07-10 [1] CRAN (R 4.2.0)
futile.options 1.0.1 2018-04-20 [1] CRAN (R 4.2.0)
genefilter * 1.80.3 2023-01-22 [1] Bioconductor
geneplotter 1.76.0 2022-11-07 [1] Bioconductor
generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
GenomeInfoDb * 1.34.9 2023-02-02 [1] Bioconductor
GenomeInfoDbData 1.2.9 2023-01-03 [1] Bioconductor
GenomicRanges * 1.50.2 2022-12-18 [1] Bioconductor
GetoptLong 1.0.5 2020-12-15 [1] CRAN (R 4.2.0)
ggforce 0.4.1 2022-10-04 [1] CRAN (R 4.2.0)
ggfun 0.1.2 2023-08-09 [1] CRAN (R 4.2.0)
ggnewscale * 0.4.9 2023-05-25 [1] CRAN (R 4.2.0)
ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.2.0)
ggplotify * 0.1.2 2023-08-09 [1] CRAN (R 4.2.0)
ggpubr * 0.6.0 2023-02-10 [1] CRAN (R 4.2.0)
ggraph 2.1.0 2022-10-09 [1] CRAN (R 4.2.0)
ggrepel * 0.9.3 2023-02-03 [1] CRAN (R 4.2.0)
ggsignif 0.6.4 2022-10-13 [1] CRAN (R 4.2.0)
ggtree 3.6.2 2022-12-04 [1] Bioconductor
GlobalOptions 0.1.2 2020-06-10 [1] CRAN (R 4.2.0)
glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
GO.db 3.16.0 2023-01-03 [1] Bioconductor
GOSemSim 2.24.0 2022-11-07 [1] Bioconductor
graph 1.76.0 2022-11-07 [1] Bioconductor
graphite 1.44.0 2022-11-07 [1] Bioconductor
graphlayouts 1.0.0 2023-05-01 [1] CRAN (R 4.2.0)
gridExtra * 2.3 2017-09-09 [1] CRAN (R 4.2.0)
gridGraphics 0.5-1 2020-12-13 [1] CRAN (R 4.2.0)
gson 0.1.0 2023-03-07 [1] CRAN (R 4.2.0)
gt * 0.9.0 2023-03-31 [1] CRAN (R 4.2.0)
gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.0)
HDO.db 0.99.1 2023-01-03 [1] Bioconductor
here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0)
highr 0.10 2022-12-22 [1] CRAN (R 4.2.0)
Hmisc * 5.1-0 2023-05-08 [1] CRAN (R 4.2.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0)
htmlTable 2.4.1 2022-07-07 [1] CRAN (R 4.2.0)
htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.0)
htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
httr 1.4.6 2023-05-08 [1] CRAN (R 4.2.2)
igraph 1.5.1 2023-08-10 [1] CRAN (R 4.2.0)
IRanges * 2.32.0 2022-11-07 [1] Bioconductor
irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.2.0)
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.0)
janitor * 2.2.0 2023-02-02 [1] CRAN (R 4.2.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
jsonlite 1.8.7 2023-06-29 [1] CRAN (R 4.2.0)
jtools * 2.2.2 2023-07-11 [1] CRAN (R 4.2.0)
KEGGREST 1.38.0 2022-11-07 [1] Bioconductor
knitr 1.43 2023-05-25 [1] CRAN (R 4.2.0)
labeling 0.4.2 2020-10-20 [1] CRAN (R 4.2.0)
lambda.r 1.2.4 2019-09-18 [1] CRAN (R 4.2.0)
lattice 0.20-45 2021-09-22 [2] CRAN (R 4.2.2)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.2.0)
lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
limma * 3.54.2 2023-03-01 [1] Bioconductor
locfit 1.5-9.8 2023-06-11 [1] CRAN (R 4.2.0)
lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.2.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
markdown 1.7 2023-05-16 [1] CRAN (R 4.2.0)
MASS 7.3-58.1 2022-08-03 [2] CRAN (R 4.2.2)
Matrix 1.5-1 2022-09-13 [2] CRAN (R 4.2.2)
MatrixGenerics * 1.10.0 2022-11-07 [1] Bioconductor
matrixStats * 1.0.0 2023-06-02 [1] CRAN (R 4.2.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
mgcv * 1.8-41 2022-10-21 [2] CRAN (R 4.2.2)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
nlme * 3.1-160 2022-10-10 [2] CRAN (R 4.2.2)
nnet 7.3-18 2022-09-28 [2] CRAN (R 4.2.2)
org.Hs.eg.db * 3.16.0 2023-01-03 [1] Bioconductor
org.Mm.eg.db * 3.16.0 2023-01-03 [1] Bioconductor
pander 0.6.5 2022-03-18 [1] CRAN (R 4.2.0)
patchwork 1.1.2 2022-08-19 [1] CRAN (R 4.2.0)
PCAtools * 2.10.0 2022-11-07 [1] Bioconductor
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
plyr 1.8.8 2022-11-11 [1] CRAN (R 4.2.0)
png 0.1-8 2022-11-29 [1] CRAN (R 4.2.0)
polyclip 1.10-4 2022-10-20 [1] CRAN (R 4.2.0)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.0)
qvalue 2.30.0 2022-11-07 [1] Bioconductor
R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.2.0)
RColorBrewer * 1.1-3 2022-04-03 [1] CRAN (R 4.2.0)
Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.2.0)
RCurl 1.98-1.12 2023-03-27 [1] CRAN (R 4.2.0)
reactome.db 1.82.0 2023-01-03 [1] Bioconductor
ReactomePA * 1.42.0 2022-11-07 [1] Bioconductor
readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.0)
reshape2 * 1.4.4 2020-04-09 [1] CRAN (R 4.2.0)
reticulate 1.28 2023-01-27 [1] CRAN (R 4.2.0)
rJava 1.0-6 2021-12-10 [1] CRAN (R 4.2.0)
rjson 0.2.21 2022-01-09 [1] CRAN (R 4.2.0)
rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
rmarkdown 2.24 2023-08-14 [1] CRAN (R 4.2.0)
rpart 4.1.19 2022-10-21 [2] CRAN (R 4.2.2)
rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0)
RSQLite 2.3.1 2023-04-03 [1] CRAN (R 4.2.0)
rstatix 0.7.2 2023-02-01 [1] CRAN (R 4.2.0)
rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.2.0)
rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.2.0)
S4Vectors * 0.36.2 2023-03-01 [1] Bioconductor
sass 0.4.7 2023-07-15 [1] CRAN (R 4.2.0)
ScaledMatrix 1.6.0 2022-11-07 [1] Bioconductor
scales * 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
scatterpie 0.2.1 2023-06-07 [1] CRAN (R 4.2.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
shadowtext 0.1.2 2022-04-22 [1] CRAN (R 4.2.0)
shape 1.4.6 2021-05-19 [1] CRAN (R 4.2.0)
snakecase 0.11.0 2019-05-25 [1] CRAN (R 4.2.0)
sparseMatrixStats 1.10.0 2022-11-07 [1] Bioconductor
stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
SummarizedExperiment * 1.28.0 2022-11-07 [1] Bioconductor
survival 3.5-5 2023-03-12 [1] CRAN (R 4.2.0)
sva * 3.46.0 2022-11-07 [1] Bioconductor
synapser * 1.0.59 2023-03-21 [1] local
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
tidygraph 1.2.3 2023-02-01 [1] CRAN (R 4.2.0)
tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
tidytree 0.4.5 2023-08-10 [1] CRAN (R 4.2.0)
timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.0)
treeio 1.22.0 2022-11-07 [1] Bioconductor
tweenr 2.0.2 2022-09-06 [1] CRAN (R 4.2.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.2)
UpSetR * 1.4.0 2019-05-22 [1] CRAN (R 4.2.0)
utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.2.0)
VennDiagram * 1.7.3 2022-04-12 [1] CRAN (R 4.2.0)
viridis 0.6.4 2023-07-22 [1] CRAN (R 4.2.0)
viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
vroom 1.6.3 2023-04-28 [1] CRAN (R 4.2.0)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
xfun 0.40 2023-08-09 [1] CRAN (R 4.2.0)
xlsx * 0.6.5 2020-11-10 [1] CRAN (R 4.2.0)
xlsxjars 0.6.1 2014-08-22 [1] CRAN (R 4.2.0)
XML 3.99-0.14 2023-03-19 [1] CRAN (R 4.2.0)
xml2 1.3.5 2023-07-06 [1] CRAN (R 4.2.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
XVector 0.38.0 2022-11-07 [1] Bioconductor
yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
yulab.utils 0.0.7 2023-08-09 [1] CRAN (R 4.2.0)
zlibbioc 1.44.0 2022-11-07 [1] Bioconductor
[1] /Users/pandera/Library/R/arm64/4.2/library
[2] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
─ Python configuration ───────────────────────────────────────────────────────
python: /Users/pandera/Library/r-miniconda-arm64/envs/r-reticulate/bin/python
libpython: /Users/pandera/Library/r-miniconda-arm64/envs/r-reticulate/lib/libpython3.8.dylib
pythonhome: /Users/pandera/Library/r-miniconda-arm64/envs/r-reticulate:/Users/pandera/Library/r-miniconda-arm64/envs/r-reticulate
version: 3.8.15 | packaged by conda-forge | (default, Nov 22 2022, 08:49:06) [Clang 14.0.6 ]
numpy: /Users/pandera/Library/r-miniconda-arm64/envs/r-reticulate/lib/python3.8/site-packages/numpy
numpy_version: 1.24.1
NOTE: Python version was forced by RETICULATE_PYTHON_FALLBACK
──────────────────────────────────────────────────────────────────────────────